Accounting for uncertainty in QTL location in marker-assisted pre-selection of young bulls prior to progeny test

The objective of this study was to evaluate whether the efficacy of marker assisted selection (MAS) could be improved by considering a confidence interval (CI) of QTL position. Specifically, MAS was applied for within-family selection in a stochastic simulation of a closed nucleus herd. The location and effect of the QTL were estimated by least squares interval mapping with a granddaughter design and marker information was then used in a top down scheme. Three approaches were used to select the best bull within full sibships of 3 or 40 bulls. All three were based on the probability of inheriting the favorable allele from the grandsire (PROB). The first method selected the sib with the highest PROB at the location with the highest F-ratio (MAX). The other two approaches were based on sums of estimated regression coefficients weighted by PROB at each cM within a 95% CI based on either bootstrapping (BOOT) or approximate LOD scores (LOD).
Accounting for CI increased the relative genetic gain in all scenarios. The average breeding value (BV) of the selected bulls was increased by 2.00, 2.60 and 2.59% when MAS was applied using MAX, BOOT and LOD, respectively, compared to random selection (h²=0.30). Selected bulls carried the correct allele in 63.0, 68.5, 67.6 and 50.1% of the cases for MAX, BOOT, LOD and random selection, respectively.     

Marker‐assisted selection reduces expected inbreeding but can result in large effects of hitchhiking

We used computer simulations to investigate to what extent true inbreeding, i.e. identity‐by‐descent, is affected by the use of marker‐assisted selection (MAS) relative to traditional best linear unbiased predictions (BLUP) selection. The effect was studied by varying the heritability (h² = 0.04 vs. 0.25), the marker distance (MAS vs. selection on the gene, GAS), the favourable QTL allele effect (α = 0.118 vs. 0.236) and the initial frequency of the favourable QTL allele (p = 0.01 vs. 0.1) in a population resembling the breeding nucleus of a dairy cattle population. The simulated genome consisted of two chromosomes of 100 cM each in addition to a polygenic component. On chromosome 1, a biallelic QTL as well as 4 markers were simulated in linkage disequilibrium. Chromosome 2 was selectively neutral. The results showed that, while reducing pedigree estimated inbreeding, MAS and GAS did not always reduce true inbreeding at the QTL relative to BLUP. MAS and GAS differs from BLUP by increasing the weight on Mendelian sampling terms and thereby lowering inbreeding, while increasing the fixation rate of the favourable QTL allele and thereby increasing inbreeding. The total outcome in terms of inbreeding at the QTL depends on the balance between these two effects. In addition, as a result of hitchhiking, MAS results in extra inbreeding in the region surrounding QTL, which could affect the overall genomic inbreeding.     

Top down preselection using marker assisted estimates of breeding values in dairy cattle

Top down preselection of young bulls before entering progeny testing has been proposed as a practicable form of marker‐assisted selection (MAS), especially in dairy cattle populations with large male paternal half‐sib families. Linkage phase between the superior (Q) and the inferior (q) QTL alleles of heterozygous sires (Qq at the QTL) with informative markers is established within each paternal half‐sib family and may be used for selection among grand‐progeny. If, additionally to sires, bulldams are also genotyped and data from consecutive generations are used, then a marker‐assisted best linear unbiased prediction (MA‐BLUP) model can be employed to connect the information of all generations and families of a top down design, and to select across all families. A customized ‘augmented’ sire model (with sires and dams of sires as random effects) is introduced for this purpose. Adapted formulae for the mixed model equations are given and their equivalence to a corresponding animal model and to a certain variant of previously proposed reduced animal models is shown. The application of the augmented sire model in MA‐BLUP estimation from daughter‐yield deviations and effective daughter contributions is presented.     

Introgressing multiple QTL in breeding programmes of limited size.

The ability to enrich a breed with favourable alleles from multiple unlinked quantitative trait loci (QTL) of a donor breed through marker-assisted introgression (MAI) in a population of limited size was evaluated by considering the effects of the proportion selected, the size of the marker intervals, the number of introgressed QTL and the uncertainty of QTL position. Informative flanking markers were used to select progeny with the largest expected number of donor QTL alleles over five generations of backcrossing and five generations of intercrossing. In the backcrossing phase, with 5% selected and 20 cM marker intervals for three QTL, there were sufficient backcross progeny that were heterozygous for all markers, and QTL frequencies dropped below 0.5 only because of double recombinants. For higher fractions selected, longer marker intervals, and more QTL, frequency reductions from 0.5 were greater and increased with additional generations of backcrossing. However, even with 20% selected, three QTL, and marker intervals of 5 or 20 cM, mean QTL frequencies in generation 5 were 0.35 and 0.30, sufficient to allow subsequent selection of QTL in the intercrossing phase. After five generations of intercrossing, over 90% of individuals were homozygous for all QTL, and 85% when five QTL were introgressed. The higher the proportions selected, the longer the marker intervals, and larger numbers of introgressed QTL increased the number of intercrossing generations required to achieve fixation of QTL. Location of the QTL in the marked intervals did not affect QTL frequencies or the proportion of QTL lost at the end of the introgression programme. In conclusion, introgressing multiple QTL can be accomplished in a MAI programme of limited size without requiring that all individuals selected during the backcrossing phase to be carriers of favourable alleles at all QTL.     

Models for marker-assisted genetic evaluation with multiple QTL in a crossbred population

The efficiency of alternative models for marker-assisted genetic evaluation with multiple previously identified QTL for a trait with heritability 0.1 was evaluated by stochastic simulation. Three biallelic unlinked additive QTL were simulated in the middle of marker intervals of 0, 10, and 20 cM, with each QTL explaining 12, 6, or 3% of genetic variance in the F₂ of a cross between inbred lines. Three models for marker-assisted genetic evaluation were compared to standard BLUP (B): BM = B with fixed marker effects; BMR = BM plus inclusion of random QTL effects; M = selection on the number of favorable marker alleles. All MAS models resulted in greater responses than B in initial generations, but extra gains declined over generations. The impact of the magnitude of QTL variance used for genetic evaluation for BMR on average QTL frequencies and response was limited. Selection with M gave greater response than B only up to the F₅. For BM and BMR, extra response over B and QTL frequencies increased when QTL effects increased and size of marker intervals decreased. The number of QTL that explained a given total amount of variance had no effect on the ranking of models in terms of QTL frequencies although a larger number of QTL resulted in higher genetic gains in later generations. Heritability had no effect on the ranking of the models. Based on genetic gains and ease of implementation, model BM is recommended as the most suitable model for marker-assisted selection in crosses of inbred lines.     

Theoretical efficiency of multiple-trait quantitative trait loci-assisted selection

The effectiveness of five selection methods for genetic improvement of net merit comprising trait 1 of low heritability (h² = 0.1) and trait 2 of high heritability (h² = 0.4) was examined: (i) two‐trait quantitative trait loci (QTL)‐assisted selection; (ii) partial QTL‐assisted selection based on trait 1; (iii) partial QTL‐assisted selection based on trait 2; (iv) QTL‐only selection; and (v) conventional selection index without QTL information. These selection methods were compared under 72 scenarios with different combinations of the relative economic weights, the genetic correlations between traits, the ratio of QTL variance to total genetic variance of the trait, and the ratio of genetic variances between traits. The results suggest that the detection of QTL for multiple‐trait QTL‐assisted selection is more important when the index traits are negatively correlated than when they are positively correlated. In contrast to literature reports that single‐trait marker‐assisted selection (MAS) is the most efficient for low heritability traits, this study found that the identified QTL of the low heritability trait contributed negligibly to total response in net merit. This is because multiple‐trait QTL‐assisted selection is designed to maximize total net merit rather than the genetic response of the individual index trait as in the case of single‐trait MAS. Therefore, it is not economical to identify the QTL of the low heritability traits for the improvement of total net merit. The efficient, cost‐effective selection strategy is to identify the QTL of the moderate or high heritability traits of the QTL‐assisted selection index to facilitate total economic returns. Detection of the QTL of the low h² traits for the QTL‐assisted index selection is justified when the low h² traits have high negative genetic correlation with the other index traits and/or when both economic weights and genetic variances of the low h² traits are larger as compared to the other index traits of higher h². This study deals with theoretical efficiency of QTL‐assisted selection, but the same principle applies to SNP‐based genomic selection when the proportion of the genetic variance ‘explained by the identified QTLs’ in this study is replaced by ‘explained by SNPs’.     

Analyzing the effect of different approaches of penalized relationship in multistage selection schemes

The aim of this study was to extend optimum contribution selection to more realistic breeding schemes with multistage selection. It seems that if the last selection stage accounts for the relationship of the selected animals, then previous selection stages also account for this relationship. An extreme example was considered here: the preselection of dairy bulls that enter a progeny testing scheme. First the penalty on the average relationship in selection step 1 is assumed the same as in step 2. Thereafter, situations with different penalties on the average relationship in the 2 selection steps were analyzed. The simulation started with the generation of prior EBV, which were sampled from a truncated normal distribution. Possible candidates for further progeny testing were selected and progeny test EBV were simulated, where the progeny test was based on 100 daughters per young bull. In situations with greater accuracy of prior EBV, high trait heritability and prior EBV were available for 2,000 bulls; the results were similar for both approaches, independent of family size. However, in a situation with low accuracy of prior EBV and low trait heritability it could be observed that with increasing penalty on the average relationship, correction for relationship in stage 1 yielded in a similar genetic level compared with selecting only for high prior EBV. If the number of bulls with prior EBV increased from 2,000 to 4,000, an increasing penalty on an average relationship gave an improved genetic level. A further improvement of the results with respect to genetic level and average relationship could be observed by increasing the penalty on an average relationship in selection step 1 above that in selection step 2. Overall, this study showed that it is beneficial to use a penalty on an average relationship already for the selection of bulls that enter the progeny test. In case optimum contribution was applied with a constraint on the average relationship in stage 2, this constraint may be translated into a penalty on the average relationship, and the current results suggested that the optimal penalty in selection stage 1 should be twice that of stage 2.     

标记辅助导入中不同背景选择方法的比较

模拟比较了随机选择、标记值选择及BLUP选择3种背景选择方法在标记辅助导入(利用标记辅助将供体群中的一个有利QTL等位基因导入到受体群中)的选择效果。前景选择是借助与目标基因连锁的两侧标记对目标基因进行间接选择。研究结果表明,在背景选择中,利用标记值选择能使受体基因组很快得到恢复,2个世代的回交就能恢复90%以上,4个世代的回交就能完全恢复。利用BLUP选择虽然不能使受体基因组迅速全部恢复,但能使特定的背景性状得到最大的遗传进展。     

Effects of a breeding scheme combined by genomic pre‐selection and progeny testing on annual genetic gain in a dairy cattle population

The effectiveness of the incorporation of genomic pre‐selection into dairy cattle progeny testing (GS‐PT) was compared with that of progeny testing (PT) where the fraction of dam to breed bull (DB) selected was 0.01. When the fraction of sires to breed bulls (SB) selected without being progeny tested to produce young bulls (YB) in the next generation was 0.2, the annual genetic gain from GS‐PT was 13% to 43% greater when h² = 0.3 and 16% to 53% greater when h² = 0.1 compared with that from PT. Given h² = 0.3, a selection accuracy of 0.8 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, GS‐PT produced 40% to 43% greater annual genetic gain than PT. Given h² = 0.1, a selection accuracy of 0.6 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, annual genetic gain from GS‐PT was 48% to 53% greater than that from PT. When h² = 0.3, progeny testing capacity had little effect on annual genetic gain from GS‐PT. However, when h² = 0.1, annual genetic gain from GS‐PT increased with increasing progeny testing capacity.     

Effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci detection and genomic evaluation in a simulated cattle population

The objective of this study was to assess the effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci (QTL) detection and genomic evaluation using a simulated cattle population. Twelve generations (G1–G12) were simulated from the base generation (G0). The recent population had different effective population sizes, heritability, and number of QTL. G0–G4 were used for pedigree information. A total of 300 genotyped bulls from G5–G10 were randomly selected. Their progenies were generated in G6–G11 with different numbers of progeny per bull. Scenarios were considered according to the number of progenies and whether the genotypes were possessed by the bulls or the progenies. A genome‐wide association study and genomic evaluation were performed with a single‐step genomic best linear unbiased prediction method to calculate the power of QTL detection and the genomic estimated breeding value (GEBV). We found that genotyped bulls could be available for QTL detection depending on conditions. Additionally, using a reference population, including genotyped bulls, which had more progeny phenotypes, enabled a more accurate prediction of GEBV. However, it is desirable to have more than 4,500 individuals consisting of both genotypes and phenotypes for practical genomic evaluation.     

Optimum contribution selection using traditional best linear unbiased prediction and genomic breeding values in aquaculture breeding schemes

The aim of this study was to compare genetic gain for a traditional aquaculture sib breeding scheme with breeding values based on phenotypic data (TBLUP) with a breeding scheme with genome-wide (GW) breeding values. Both breeding schemes were closed nuclei with discrete generations modeled by stochastic simulation. Optimum contribution selection was applied to restrict pedigree-based inbreeding to either 0.5 or 1% per generation. There were 1,000 selection candidates and a sib test group of either 4,000 or 8,000 fish. The number of selected dams and sires to create full sib families in each generation was determined from the optimum contribution selection method. True breeding values for a trait were simulated by summing the number of each QTL allele and the true effect of each of the 1,000 simulated QTL. Breeding values in TBLUP were predicted from phenotypic and pedigree information, whereas genomic breeding values were computed from genetic markers whose effects were estimated using a genomic BLUP model. In generation 5, genetic gain was 70 and 74% greater for the GW scheme than for the TBLUP scheme for inbreeding rates of 0.5 and 1%. The reduction in genetic variance was, however, greater for the GW scheme than for the TBLUP scheme due to fixation of some QTL. As expected, accuracy of selection increased with increasing heritability (e.g., from 0.77 with a heritability of 0.2 to 0.87 with a heritability of 0.6 for GW, and from 0.53 and 0.58 for TBLUP in generation 5 with sib information only). When the trait was measured on the selection candidate compared with only on sibs and the heritability was 0.4, accuracy increased from 0.55 to 0.69 for TBLUP and from 0.83 to 0.86 for GW. The number of selected sires to get the desired rate of inbreeding was in general less in GW than in TBLUP and was 33 for GW and 83 for TBLUP (rate of inbreeding 1% and heritability 0.4). With truncation selection, genetic gain for the scheme with GW breeding values was nearly twice as large as a scheme with traditional BLUP breeding values. The results indicate that the benefits of applying GW breeding values compared with TBLUP are reduced when contributions are optimized. In conclusion, genetic gain in aquaculture breeding schemes with optimized contributions can increase by as much as 81% by applying genome-wide breeding values compared with traditional BLUP breeding values.     

Efficiency of selection strategies for halothane-negative gene

Use of a method to estimate the frequency of the halothane-negative allele in boars is illustrated for different sampling schemes for boar testing programs and for testing within closed breeding populations. This method uses information not only on the individual, but also on all mates and relatives including parents, siblings and offspring. Accuracy of the estimates of the allelic frequency in boars was measured through use of Monte Carlo simulation. The selection differential in real frequency of the halothane-negative allele when boars were selected on estimated allelic frequency was used as the criterion for accuracy. In the progeny testing situation, phenotypes of base boars and one generation of offspring were available. The average selection differentials with 90% selection (i.e., culling 10% of boars on estimated allelic frequency) when 2 and 10 litters of two boars each were tested were .017 and .044 in base boars and .013 and .025 in the offspring. The value of the boar's own phenotype was small. Higher selection differentials were found in the closed herd situation, where data on two generations were available. The selection differential in base boars when 10 litters were tested increased from .046 to .066 when the proportion of boars selected decreased from 90% to 50%. No improvement in selection differential with proportion selected was found in the progeny testing situation. Intense selection is most effective when the number of litters per boar is large and data over several generations are used. The estimation procedure for allelic frequencies in boars should improve current screening and selection programs to reduce halothane sensitivity in pigs.     

Use of marker haplotypes to refine covariances among relatives for breeding value estimation

Markers flanking DNA regions, where quantitative trait loci (QTL) have been previously spotted, can be used to trace the common inheritance of major genes for a better definition of covariances among animals. A practical approach to the use of marker data to refine the additive relationship matrix used in the traditional best linear unbiased prediction (BLUP) methodology is presented. The technique allows the number of the mixed model equations to be kept to an animal level, blending polygenic pedigree data with marker haplotype information. The advantage of this marker-assisted selection (MAS) approach over BLUP selection has been assessed through a stochastic simulation. A finite locus model with 32 independent biallelic loci was generated with normally distributed allelic effects. The heritability of the trait, measured on both sexes and on females only, was set to 0.2 and 0.5. Five-allelic markers 2, 10 and 20 cM apart, bracketed the QTL with the largest effect on the trait, accounting for 17% of the genetic variance. The bracketed QTL had two or eight alleles and its position was undefined within the bracket. Results show a moderate 2% advantage of MAS over BLUP in terms of higher genetic response when trait was recorded on both sexes and heritability was 0.2. The benefit is in the short term, but it lasts longer with polyallelic QTL. When the trait was recorded on females only, MAS produced only a small and insignificant genetic gain, but reduced the overall inbreeding in the population. MAS was also inefficient when heritability was 0.5.     

Prospects and strategies for genetic improvement of the dairy potential of tropical cattle by selection

The import of genetic material and the use of crossbreeding to improve the dairy merit of tropical cattle has been criticized for eroding the livestock genetic resources of the tropics. An alternative is genetic improvement of the indigenous cattle through selection. The objective of the present paper is to examine the feasibility of this alternative.Constraints to genetic improvement of tropical cattle through selection are discussed. Low reproductive rates and high calf mortality reduce the intensity of selection. The generation interval, which is long in cattle, is further prolonged by the late sexual maturity and the long calving intervals in most tropical breeds. The most serious constraint is, however, that the extensive milk recording schemes which support dairy cattle breeding programmes in many temperate countries are almost non-existent in the tropics. In this situation, the most realistic approach to improvement through selection is to start with a single nucleus herd (or a group of cooperating herds).Two alternative selection programmes (with and without progeny testing) for a closed herd of 500 cows are outlined. The alternative which assumed no progeny testing, i.e. selection of bulls on pedigree information only, gave the fastest genetic improvement (predicted at 36 kg/year).By distributing breeding bulls from the herd the genetic progress can be disseminated to the outside population with a time lag of about two generations. A nucleus herd can supply about 15 selected bulls per 100 cows in the herd per year, enough for a population of several thousand cows.     

Optimization of mate selection based on genotypic information with overlapping generations

The objective of this study was to develop a method to optimize the selection and mating decisions based on genotypic information with overlapping generations by applying mate selection algorithm. In this study, differential evolution algorithm was used to optimize numbers of mating between genotypes and the optimal numbers of males and females selected from each genotype over planning horizon. This method assumed a single biallelic QTL ( Q and q ) and was applied to a simple situation in a herd of pigs as an example. Four dominance degrees of gene: recessive, additive, complete-dominance and over-dominance were considered. For all dominance degrees, the frequency of the favourable allele in males selected for replacement increased rapidly. In contrast, the frequency of favourable allele in females selected for replacement increased more gradually. The superiorities in cumulative discounted performance (CDP) when the initial allele frequency was 0.5 over a case when the frequency was 0.05 over the planning horizon were greatly affected by dominance degrees. The CDP superiorities ranged from 24% for an over-dominant QTL to 91% for a recessive QTL.     

DNA-based paternity analysis and genetic evaluation in a large, commercial cattle ranch setting

Deoxyribonucleic acid-based tests were used to assign paternity to 625 calves from a multiple-sire breeding pasture. There was a large variability in calf output and a large proportion of young bulls that did not sire any offspring. Five of 27 herd sires produced over 50% of the calves, whereas 10 sires produced no progeny and 9 of these were yearling bulls. A comparison was made between the paternity results obtained when using a DNA marker panel with a high (0.999), cumulative parentage exclusion probability (P(E)) and those obtained when using a marker panel with a lower P(E) (0.956). A large percentage (67%) of the calves had multiple qualifying sires when using the lower resolution panel. Assignment of the most probable sire using a likelihood-based method based on genotypic information resolved this problem in approximately 80% of the cases, resulting in 75% agreement between the 2 marker panels. The correlation between weaning weight, on-farm EPD based on pedigrees inferred from the 2 marker panels was 0.94 for the 24 bulls that sired progeny. Partial progeny assignments inferred from the lower resolution panel resulted in the generation of EPD for bulls that actually sired no progeny according to the high-P(E) panel, although the Beef Improvement Federation accuracies of EPD for these bulls were never greater than 0.14. Simulations were performed to model the effect of loci number, minor allele frequency, and the number of offspring per bull on the accuracy of genetic evaluations based on parentage determinations derived from SNP marker panels. The SNP marker panels of 36 and 40 loci produced EPD with accuracies nearly identical to those EPD resulting from use of the true pedigree. However, in field situations where factors including variable calf output per sire, large sire cohorts, relatedness among sires, low minor allele frequencies, and missing data can occur concurrently, the use of marker panels with a larger number of SNP loci will be required to obtain accurate on-farm EPD.     

Two-stage selection strategies utilizing marker-quantitative trait locus information and individual performance.

Short- and long-term response to marker-assisted selection in two stages was studied using a stochastic simulation of a closed nucleus herd for beef production. First-stage selection was carried out within families based on information at a fully additive quantitative trait locus (QTL). Second-stage selection strategies were based on 1) individual phenotype, 2) individual phenotype precorrected for QTL, 3) a selection index incorporating phenotype and QTL information, 4) a standard animal model BLUP, and 5) a selection index incorporating marker-QTL information and standard animal model BLUP on records precorrected for QTL. All strategies were efficient in moving the favorable allele at the QTL to fixation, but they differed in the time to reach fixation. Mass selection was less efficient in changing allele frequencies than BLUP. Discounted accumulated response, accounting for the time response was realized and inflation rate, was proposed to rank strategies and to elude the conflict between short- and long-term response in marker-assisted selection. Discounted accumulated response at a time horizon of 20 yr for alternative two-stage selection strategies was compared with conventional BLUP carried out in second stage only. Within-family selection increased discounted accumulated response by more than 11% using Strategy 4 and by up to 12% using Strategy 5 at an inflation rate of 2%. The percentage increase in response was less for highly heritable traits and when the proportion of additive variance explained by the QTL was small. Strategy 5 gave larger response with reduced inbreeding. This strategy also resulted in the lowest cost-benefit ratio, requiring less genotyping per unit of response. Cost-benefit ratio for discounted genotyping and for discounted in vitro production of embryos for traits with low heritability was two to four times that for traits with high heritability. The use of first-stage selection slightly increased the level of inbreeding for both mass (Strategy 1) and BLUP selection (Strategies 4 and 5).     

Consequences of selection for improving production traits on the frequency of deleterious alleles for fitness

In this study the effect of artificial selection on BLUP EBV for production traits on the allele frequencies of a pleiotropic QTL affecting both production and disease susceptibility was investigated. Stochastic simulations were used to model artificial selection on a production trait that is controlled, in part, by a biallelic QTL that also controls susceptibility to disease. The QTL allele increasing production also increased susceptibility to disease. Different modes of action and proportions of variation accounted for by the QTL were assessed for the production trait. The main results indicated that alleles that confer susceptibility to the disease could be maintained in the population over a long period, depending on the mode of action of the QTL. In addition, the results of the study indicate that, under various conditions, it is possible to find pleiotropic QTL that control 2 traits despite these traits appearing to be uncorrelated. Therefore, in practice, an estimate of the genetic correlation between 2 traits may be misleading when the presence of such a QTL exists. The results of this study have implications for breeding programs. For example, if a pleiotropic QTL exists that favors heterozygotes for a production trait, it would be very difficult to remove disease susceptibility alleles via traditional selection methods.     

Interval mapping of growth quantitative trait loci in Japanese Black beef cattle using microsatellite DNA markers and half-sib regression analysis

A confirmatory scan for the regions of bovine chromosome 1 segregating the quantitative trait loci (QTL) influencing birthweight, weaning weight, yearling weight, and preweaning and postweaning average daily gains was performed by genotyping half‐sib progeny of four Japanese Black sires using microsatellite DNA markers. Data were analyzed by generating an F‐statistic every 1 cM on a linkage map by the regression of phenotype on the probabilities of inheriting an allele from the sire after adjusting for the fixed effects of sire, sex, parity and season of birth as well as age as a covariate. Permutation tests at chromosome‐wide significance thresholds were carried out over 10 000 iterations. A significant QTL for birthweight at 114 cM was detected in the sire 2 family. This identification of a birthweight QTL in Japanese Black cattle may be useful for the implementation of marker‐assisted selection.     

Prediction of response to marker-assisted and genomic selection using selection index theory

Selection index methods can be used for deterministic assessment of the potential benefit of including marker information in genetic improvement programmes using marker-assisted selection (MAS). By specifying estimates of breeding values derived from marker information (M-EBV) as a correlated trait with heritability equal to 1, it was demonstrated that marker information can be incorporated in standard software for selection index predictions of response and rates of inbreeding, which requires specifying phenotypic traits and their genetic parameters. Path coefficient methods were used to derive genetic and phenotypic correlations between M-EBV and the phenotypic data. Methods were extended to multi-trait selection and to the case when M-EBV are based on high-density marker genotype data, as in genomic selection. Methods were applied to several example scenarios, which confirmed previous results that MAS substantially increases response to selection but also demonstrated that MAS can result in substantial reductions in the rates of inbreeding. Although further validation by stochastic simulation is required, the developed methodology provides an easy means of deterministically evaluating the potential benefits of MAS and to optimize selection strategies with availability of marker data.